当前位置: X-MOL 学术IEEE/ACM Trans. Comput. Biol. Bioinform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Generative Incomplete Multi-View Prognosis Predictor for Breast Cancer: GIMPP
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2021-06-18 , DOI: 10.1109/tcbb.2021.3090458
Nikhilanand Arya 1 , Sriparna Saha 1
Affiliation  

In today's digital world, we are equipped with modern computer-based data collection sources and feature extraction methods. It enhances the availability of the multi-view data and corresponding researches. Multi-view prediction models form a mainstream research direction in the healthcare and bioinformatics domain. While these models are designed with the assumption that there is no missing data for any views, in the real world, certain views of the data are often not having the same number of samples, resulting in the incomplete multi-view dataset. The studies performed over these datasets are termed incomplete multi-view clustering or prediction. Here, we develop a two-stage generative incomplete multi-view prediction model named GIMPP to address the missing view problem of breast cancer prognosis prediction by explicitly generating the missing data. The first stage incorporates the multi-view encoder networks and the bi-modal attention scheme to learn common latent space representations by leveraging complementary knowledge between different views. The second stage generates missing view data using view-specific generative adversarial networks conditioned on the shared representations and encoded features given by other views. Experimental results on TCGA-BRCA and METABRIC datasets proves the usefulness of the developed method over the state-of-the-art methods.

中文翻译:

乳腺癌的生成不完整多视图预后预测器:GIMPP

在当今的数字世界中,我们配备了现代基于计算机的数据收集源和特征提取方法。它提高了多视图数据和相应研究的可用性。多视图预测模型形成了医疗保健和生物信息学领域的主流研究方向。虽然这些模型的设计假设任何视图都没有丢失数据,但在现实世界中,数据的某些视图通常没有相同数量的样本,从而导致不完整的多视图数据集。对这些数据集进行的研究称为不完整的多视图聚类或预测。这里,我们开发了一个名为 GIMPP 的两阶段生成不完全多视图预测模型,通过显式生成缺失数据来解决乳腺癌预后预测的缺失视图问题。第一阶段结合了多视图编码器网络和双模式注意方案,通过利用不同视图之间的互补知识来学习常见的潜在空间表示。第二阶段使用特定于视图的生成对抗网络生成缺失的视图数据,该网络以其他视图给出的共享表示和编码特征为条件。TCGA-BRCA 和 METABRIC 数据集的实验结果证明了所开发的方法优于最先进的方法。第一阶段结合了多视图编码器网络和双模式注意方案,通过利用不同视图之间的互补知识来学习常见的潜在空间表示。第二阶段使用特定于视图的生成对抗网络生成缺失的视图数据,该网络以其他视图给出的共享表示和编码特征为条件。TCGA-BRCA 和 METABRIC 数据集的实验结果证明了所开发的方法优于最先进的方法。第一阶段结合了多视图编码器网络和双模式注意方案,通过利用不同视图之间的互补知识来学习常见的潜在空间表示。第二阶段使用特定于视图的生成对抗网络生成缺失的视图数据,该网络以其他视图给出的共享表示和编码特征为条件。TCGA-BRCA 和 METABRIC 数据集的实验结果证明了所开发的方法优于最先进的方法。
更新日期:2021-06-18
down
wechat
bug